AI Empowerment and Indigenous Software Integration: A New Paradigm for New-Form Textbook Development in Numerical Computation Courses

Abstract

The diversification of academic software accessibility has prompted higher education institutions to explore indigenous alternatives for scientific computing platforms, while national policies on educational digitization mandate the integration of artificial intelligence across all teaching elements. Numerical computation courses, serving as a bridge between mathematical theory and engineering applications, must adapt to these dual imperatives of software diversification and AI integration. This paper proposes a three-dimensional textbook development framework: systematic integration of indigenous software platforms as primary computing engines, embedding of AI-powered case studies that demonstrate how classical numerical algorithms underpin modern machine learning applications, and development of new-form digital textbooks featuring interactive visualizations, executable code snippets, and intelligent assessment capabilities. This paper presents concrete implementation strategies, including algorithm library development, AI-infused case design, and digital resource construction. The framework aims to cultivate students’ proficiency in diverse software ecosystems while enhancing their understanding of the “AI for Science” paradigm, thereby serving both pedagogical innovation and the broader goal of educational resource diversification.